Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks.

From bacteria following simple chemical gradients to the brain distinguishing complex odour information, the ability to recognize molecular patterns is essential for biological organisms. This type of information-processing function has been implemented using DNA-based neural networks, but has been limited to the recognition of a set of no more than four patterns, each composed of four distinct DNA molecules. Winner-take-all computation has been suggested as a potential strategy for enhancing the capability of DNA-based neural networks.

Compiler-aided systematic construction of large-scale DNA strand displacement circuits using unpurified components.

Biochemical circuits made of rationally designed DNA molecules are proofs of concept for embedding control within complex molecular environments. They hold promise for transforming the current technologies in chemistry, biology, medicine and material science by introducing programmable and responsive behaviour to diverse molecular systems. As the transformative power of a technology depends on its accessibility, two main challenges are an automated design process and simple experimental procedures.

Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation.

Automated computer-aided detection (CADe) has been an important tool in clinical practice and research. State-of-the-art methods often show high sensitivities at the cost of high false-positives (FP) per patient rates. We design a two-tiered coarse-to-fine cascade framework that first operates a candidate generation system at sensitivities  ∼ 100% of but at high FP levels.

Sequential Monte Carlo tracking of the marginal artery by multiple cue fusion and random forest regression.

Given the potential importance of marginal artery localization in automated registration in computed tomography colonography (CTC), we have devised a semi-automated method of marginal vessel detection employing sequential Monte Carlo tracking (also known as particle filtering tracking) by multiple cue fusion based on intensity, vesselness, organ detection, and minimum spanning tree information for poorly enhanced vessel segments. We then employed a random forest algorithm for intelligent cue fusion and decision making which achieved high sensitivity and robustness. After applying a vessel pruning procedure to the tracking results, we achieved statistically significantly improved precision compared to a baseline Hessian detection method (2.

2D view aggregation for lymph node detection using a shallow hierarchy of linear classifiers.

Enlarged lymph nodes (LNs) can provide important information for cancer diagnosis, staging, and measuring treatment reactions, making automated detection a highly sought goal. In this paper, we propose a new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue. Our 2D detection can be effectively formulated as linear classification on a single image feature type of Histogram of Oriented Gradients (HOG), covering a moderate field-of-view of 45 by 45 voxels.

A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations.

Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely distributed locations. State-of-the-art studies show the performance range of 52.9% sensitivity at 3.

Ligand-protein interactions of selective casein kinase 1δ inhibitors.

Casein kinase 1δ (CK1δ) and 1ε (CK1ε) are believed to be necessary enzymes for the regulation of circadian rhythms in all mammals. On the basis of our previously published work demonstrating a CK1ε-preferring compound to be an ineffective circadian clock modulator, we have synthesized a series of pyrazole-substitued pyridine inhibitors, selective for the CK1δ isoform. Additionally, using structure-based drug design, we have been able to exploit differences in the hinge region between CK1δ and p38 to find selective inhibitors that have minimal p38 activity.

Characterization of resilin-based materials for tissue engineering applications.

Modular proteins have emerged as powerful tools in tissue engineering because both the mechanical and biochemical properties can be precisely controlled through amino acid sequence. Resilin is an attractive candidate for use in modular proteins because it is well-known for having low stiffness, high fatigue lifetime, and high resilience. However, no studies have been conducted to assess resilin's compressive properties, cytocompatibility with clinically relevant cells, or effect on cell spreading.

Preventing rapid repeat births among latina adolescents: the role of parents.

Latina adolescent parents are at increased risk for rapid repeat births (second birth ≤ 24 months after the first), sexually transmitted infections, and negative educational and social outcomes. Although several effective parent-based interventions have been developed to prevent Latino youths' sexual risk taking, little research has explored the development of interventions to prevent repeat births that involve the parents of these adolescents. Existing preventative interventions involving parents suffer from important methodological limitations.

Modular cloning and protein expression of long, repetitive resilin-based proteins.

Resilin has emerged as a promising new biomaterial possessing attractive properties for tissue engineering applications. To date, proteins with repeating resilin motifs have been expressed with molecular weights less than 30 kDa. This work describes the development of resilin-based proteins (repeating motif derived from Anopheles gambiae) 50 kDa in size.

Thiazole-diamides as potent gamma-secretase inhibitors.

The thiazole-diamide series (1) has been identified as highly potent gamma-secretase inhibitors. Several representative compounds showed IC(50) values of <0.3 nM.